Conventional optimization based depth-from-focus approaches [1]}, [2]}, [3]} estimate the level of sharpness for each pixel and often suffer from textureless objects or aperture problems.
Deep learning techniques [4]}, [5]} help to overcome these issues but need ground truth depth data for supervised learning.
It's difficult and time-consuming to retrieve focal stacks with registered depth maps, let alone the imperfect depth data obtained by hardware solutions such as ToF sensors [4]}.
One could synthesize defocus blur on a synthetic dataset with synthetic depth maps [5]}.
However, it is still questionable whether the thin lens synthesis model could represent real-world optics well.
